Cross-view object tracking by projective invariants and feature matching

Abstract

One of the key techniques of multi-camera tracking systems is cross-view object tracking. Feature Matching (FM) and Field of View (FOV) based methods are adopted in conventional solutions towards this problem. However, FM is not computationally efficient and the results heavily depend on the parameter settings of the cameras. Therefore, it is not effective in practical applications. In addition, approaches based on FOV suffer from the delay of the detection of newly appeared objects. The results are not reliable if only consistent labelling is utilized. In this paper, we propose a novel scheme for cross-view object tracking based on Projective Invariants (PI) and FM. The experimental results show that, our method improves the performance of normal PI-based tracking algorithms. Especially, it provides accurate tracking performance in the case of multiple objects appear closely in the same area. © 2008 Springer.